Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Author: search.filters.author.Gharbi, Kheira

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    Inhibition of asphaltene flocculation in light crude oil: effect of pressure, temperature and inhibitor concentration
    (Taylor and Francis Online, 2018) Gharbi, Kheira; Benyounes, Khaled; Benamara, Chahrazed
    In this study, a light crude oil sample was taken from Hassi Messaoud field to characterize its physicochemical properties. The asphaltene flocculation onset was determined in the dead oil by Flocculation Titrimeter equipment. The petroleum resins have been extracted from the same crude oil and tested as an inhibitor of asphaltene flocculation then their efficiency has been studied at different conditions of pressure and temperature. The results point out that the extracted resins may have two different effects on the onset point depending upon the operator conditions and the concentration of the added resins to crude oil.
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    Modeling wax disappearance temperature using advanced intelligent frameworks
    (American Chemical Society, 2019) Benamara, Chahrazed; Nait Amar, Menad; Gharbi, Kheira; Hamada, Boudjema
    The deposition of wax is one of the most potential problems that disturbs the flow assurance during production processes of hydrocarbon fluids. In this study, wax disappearance temperature (WDT) that is recognized as a vital parameter in such circumstances is modeled using advanced machine learning techniques, namely, radial basis function neural network (RBFNN) coupled with genetic algorithm (GA) and artificial bee colony (ABC). Besides, an accurate and user-friendly correlation was established by implementing the group method of data handling. Results revealed the high reliability of the proposed hybrid models and the established correlation. Moreover, RBFNN coupled with ABC (RBFNN-ABC) was found to be the best paradigm with an overall average absolute relative error value of 0.5402% and a total coefficient of determination (R2) of 0.9706. Furthermore, the performance comparison showed that RBFNN-ABC and the established explicit correlation outperform the prior intelligent and thermodynamic models. Finally, by performing the outlier detection, the quality of the utilized database was assessed, the applicability realm of the best model was delineated, and only one point was found as doubtful
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    Removal and prevention of asphaltene deposition during oil production : a literature review
    (Elsevier, 2017) Gharbi, Kheira; Benyounes, Khaled; Khodja, Mohamed